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https://github.com/hwchase17/langchain
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3f0357f94a
# Add summarization task type for HuggingFace APIs Add summarization task type for HuggingFace APIs. This task type is described by [HuggingFace inference API](https://huggingface.co/docs/api-inference/detailed_parameters#summarization-task) My project utilizes LangChain to connect multiple LLMs, including various HuggingFace models that support the summarization task. Integrating this task type is highly convenient and beneficial. Fixes #4720
119 lines
3.5 KiB
Python
119 lines
3.5 KiB
Python
"""Test Self-hosted LLMs."""
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import pickle
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from typing import Any, List, Optional
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from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
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from langchain.llms import SelfHostedHuggingFaceLLM, SelfHostedPipeline
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model_reqs = ["pip:./", "transformers", "torch"]
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def get_remote_instance() -> Any:
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"""Get remote instance for testing."""
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import runhouse as rh
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return rh.cluster(name="rh-a10x", instance_type="A100:1", use_spot=False)
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def test_self_hosted_huggingface_pipeline_text_generation() -> None:
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"""Test valid call to self-hosted HuggingFace text generation model."""
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gpu = get_remote_instance()
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llm = SelfHostedHuggingFaceLLM(
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model_id="gpt2",
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task="text-generation",
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model_kwargs={"n_positions": 1024},
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hardware=gpu,
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model_reqs=model_reqs,
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)
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output = llm("Say foo:") # type: ignore
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assert isinstance(output, str)
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def test_self_hosted_huggingface_pipeline_text2text_generation() -> None:
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"""Test valid call to self-hosted HuggingFace text2text generation model."""
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gpu = get_remote_instance()
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llm = SelfHostedHuggingFaceLLM(
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model_id="google/flan-t5-small",
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task="text2text-generation",
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hardware=gpu,
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model_reqs=model_reqs,
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)
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output = llm("Say foo:") # type: ignore
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assert isinstance(output, str)
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def test_self_hosted_huggingface_pipeline_summarization() -> None:
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"""Test valid call to self-hosted HuggingFace summarization model."""
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gpu = get_remote_instance()
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llm = SelfHostedHuggingFaceLLM(
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model_id="facebook/bart-large-cnn",
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task="summarization",
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hardware=gpu,
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model_reqs=model_reqs,
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)
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output = llm("Say foo:")
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assert isinstance(output, str)
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def load_pipeline() -> Any:
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"""Load pipeline for testing."""
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model_id = "gpt2"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id)
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pipe = pipeline(
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"text-generation", model=model, tokenizer=tokenizer, max_new_tokens=10
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)
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return pipe
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def inference_fn(pipeline: Any, prompt: str, stop: Optional[List[str]] = None) -> str:
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"""Inference function for testing."""
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return pipeline(prompt)[0]["generated_text"]
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def test_init_with_local_pipeline() -> None:
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"""Test initialization with a self-hosted HF pipeline."""
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gpu = get_remote_instance()
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pipeline = load_pipeline()
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llm = SelfHostedPipeline.from_pipeline(
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pipeline=pipeline,
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hardware=gpu,
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model_reqs=model_reqs,
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inference_fn=inference_fn,
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)
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output = llm("Say foo:") # type: ignore
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assert isinstance(output, str)
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def test_init_with_pipeline_path() -> None:
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"""Test initialization with a self-hosted HF pipeline."""
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gpu = get_remote_instance()
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pipeline = load_pipeline()
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import runhouse as rh
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rh.blob(pickle.dumps(pipeline), path="models/pipeline.pkl").save().to(
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gpu, path="models"
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)
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llm = SelfHostedPipeline.from_pipeline(
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pipeline="models/pipeline.pkl",
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hardware=gpu,
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model_reqs=model_reqs,
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inference_fn=inference_fn,
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)
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output = llm("Say foo:") # type: ignore
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assert isinstance(output, str)
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def test_init_with_pipeline_fn() -> None:
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"""Test initialization with a self-hosted HF pipeline."""
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gpu = get_remote_instance()
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llm = SelfHostedPipeline(
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model_load_fn=load_pipeline,
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hardware=gpu,
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model_reqs=model_reqs,
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inference_fn=inference_fn,
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)
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output = llm("Say foo:") # type: ignore
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assert isinstance(output, str)
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